Business-Applied Statistical Results

To address the need for managing regulatory risks and optimizing commercial offerings, I executed Stress Testing and BUST (Bottom-Up Stress Test) for SUGEF. My primary role involved projecting key macroeconomic variables—such as inflation, interest rates, and exchange rates—where I successfully synthesized statistical results with senior management’s expert judgment. This balance ensured that projections were not merely figures, but validated scenarios accepted by the business for critical decision-making.

Rather than providing “black box” models, I delivered transparent tools that enabled managers to clearly identify the right products for each client. By automating these reports and engineering high-value variables through Machine Learning, I transformed the Data Warehouse into a solutions engine that aligns technical rigor with the organization’s strategic objectives.

Statistical Consulting for Strategic Decision-Making

As a specialist consultant in statistics and data intelligence, I have led high-complexity projects for international organizations (UNICEF, UNFPA, IDB) and government institutions, transforming massive datasets into strategic assets. My intervention has been pivotal in national milestones, ranging from the technical validation of the 2022 Census—through the processing of territorial microdata—to the methodological design of the Child and Adolescent Well-being Index (IBINA). By leveraging advanced classification models, statistical inference, and the development of custom visualization applications in R, I have optimized the capacity of governments and organizations to ground public policies and investment plans in solid evidence.

My experience transcends borders, having collaborated on international consulting projects within global work ecosystems, where I have executed everything from critical descriptive analyses to predictive models for digital educational transformation in Latin America. By integrating scientific rigor with a results-oriented vision, I have successfully turned complex data structures into intuitive management tools, ensuring that every statistical finding translates into a competitive advantage or a measurable social impact. My focus is not merely on processing information, but on designing the analytical infrastructure that enables organizations to lead in the data era.

Applied Statistics in the Political-Electoral Field

Within the framework of the 2018 Costa Rican national elections—marked by unprecedented political volatility—I collaborated with the CIEP-UCR research team to strengthen the robustness of the post-election study. My contribution was fundamental during the design phase, where I helped formulate strategic questions for the national survey of 1,500 individuals, ensuring that the variables captured the new dynamics of citizen behavior with precision. I was responsible for transforming complex data into actionable insights through the creation of data visualizations and the execution of descriptive statistical analyses. These tools allowed for the identification of key patterns in social discontent and participation, facilitating a clear interpretation of the sociodemographic and attitudinal factors that influenced the electoral process. My work ensured that the project’s findings were presented with scientific rigor and high visual clarity, consolidating this study’s position as the most technically credible benchmark in the country.

Higher Education Instructor

Faced with the growing gap between massive data accumulation and the extraction of real value, I led the design and execution of advanced technical analytics programs at the University of Costa Rica. My objective was to empower multidisciplinary professionals to overcome the challenge of processing raw data and converting it into strategic assets using cutting-edge tools and agile methodologies such as CRISP-DM.

I optimized learning processes by implementing advanced environments in R, Python, and SQL, focusing on automated data capture (CSPro), high-impact visualization, and the deployment of Machine Learning models for predictive classification. I successfully certified groups of experts in large-scale data manipulation and model validation under Git version control standards, ensuring 100% reproducibility in statistical analysis and a substantial improvement in the accuracy of inferences for evidence-based decision-making.

In this role, I served as a lecturer for the following courses:

• Principles of Machine Learning for Classification Problems with R

• Statistical Programming with R

• Computational Statistics II

• General Statistics II

• Introductory Statistics II

• Introductory Statistics I

Applied Statistical Analysis for Industry

In a business environment saturated with information, the primary challenge was to train future managers to transform raw data into actionable business intelligence. I led the design and execution of the Probability and Statistics I program, where I streamlined the transfer of technical knowledge toward practical applications in the economic field. My intervention focused on developing critical competencies, ranging from the structuring of statistical research and data collection instrument design to the mastery of advanced probability models ($Binomial$, $Normal$, and $Poisson$) and measures of variability.To ensure academic excellence, I implemented a continuous evaluation system based on real-world case studies and complex problem-solving. This approach ensured that students not only mastered the mathematics but also learned to interpret data symmetry and behavior for strategic decision-making. The result was a comprehensive academic success rate, with 100% of the evaluation tied to practical application, culminating in final reports that met professional quality standards and statistical rigor.

Population Estimates and Projections

Faced with the challenge of updating population estimates and projections for 1950–2100 amidst accelerating demographic shifts and complex datasets, I led the project’s methodological and technological development to ensure the accuracy of public policies. I optimized institutional capacity by creating and publishing the popstudy R package on CRAN. This tool automated the processing of administrative records and the implementation of advanced models, such as Lee-Carter for mortality and K-means clustering for functional data analysis.

This intervention not only modernized the institution’s analytical infrastructure but also exponentially increased operational efficiency by migrating critical processes from SPSS to R. The direct impact is seen in the delivery of a robust, comparable methodology that enables continuous monitoring of national demographic dynamics through the year 2100, ensuring high-quality data for the country’s inclusive and sustainable development.

Measurement Strategist

Transforming Judicial Service Perception into High-Impact Decisions

The Judiciary of Costa Rica faced a critical information gap: a lack of specific data regarding quality perception within the Judicial Auxiliary sector, which hindered decision-making aligned with the Institutional Strategic Plan. To address this challenge, I led the methodological and technical design of a research project focused on capturing the voice of the external user (citizens). My intervention centered on creating a robust measurement instrument grounded in ten key quality factors—such as reliability, responsiveness, and assurance—ensuring the technical traceability required to evaluate sensitive services like those of the Public Prosecutor’s Office and the Judicial Investigation Department (OIJ).

As a direct result, I developed a replicable methodological framework, including a specialized sampling design and multivariate analysis tools for critical data tabulation. This project not only provided the first authentic diagnosis of perceived service quality but also delivered a strategic asset to the Service Comptroller’s Office to optimize public management and strengthen the social legitimacy of the justice system.

Statistical Support for Research Projects and University Courses

• INISA: Analysis of parasitic status in children within the South-Central region (2016-2017)
• Interrelations between public policy and migration (2015)
• Statistics for Social Sciences (2014 – 2016)
• Introductory Statistics (2014 – 2016)
• Statistics for Biologists (2015-2016)
• Tutoring for Social Sciences and Health Sciences students (2014-2016).
• Probability and Statistical Inference (2013 – 2014)
• Applied Regression Models (2016)